scholarly journals Construction and Comprehensive Analysis of a Stratification System Based on AGTRAP in Patients with Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Li Wang ◽  
Wenjun Zhang ◽  
Tao Yang ◽  
Le He ◽  
Yunmei Liao ◽  
...  

Background. With the development of sequencing technology, several signatures have been reported for the prediction of prognosis in patients with hepatocellular carcinoma (HCC). However, the above signatures are characterized by cumbersome application. Therefore, the study is aimed at screening out a robust stratification system based on only one gene to guide treatment. Methods. Firstly, we used the limma package for performing differential expression analysis on 374 HCC samples, followed by Cox regression analysis on overall survival (OS) and disease-free interval (PFI). Subsequently, hub prognostic genes were found at the intersection of the above three groups. In addition, the topological degree inside the PPI network was used to screen for a unique hub gene. The rms package was used to construct two visual stratification systems for OS and PFI, and Kaplan-Meier analysis was utilized to investigate survival differences in clinical subgroups. The ssGSEA algorithm was then used to reveal the relationship between the hub gene and immune cells, immunological function, and checkpoints. In addition, we also used function annotation to explore into putative biological functions. Finally, for preliminary validation, the hub gene was knocked down in the HCC cell line. Results. We discovered 6 prognostic genes (SKA1, CDC20, AGTRAP, BIRC5, NEIL3, and CDC25C) for constructing a PPI network after investigating survival and differential expression genes. According to the topological degree, AGTRAP was chosen as the basis for the stratification system, and it was revealed to be a risk factor with an independent prognostic value in Kaplan-Meier analysis and Cox regression analysis ( P < 0.05 ). In addition, we constructed two visualized nomograms based on AGTRAP. The novel stratification system had a robust predictive value for PFI and OS in ROC analysis and calibration curve ( P < 0.05 ). Meanwhile, AGTRAP upregulation was associated with T staging, N staging, M staging, pathological stage, grade, and vascular invasion ( P < 0.05 ). Notably, AGTRAP was overexpressed in tumor tissues in all pancancers with paired samples ( P < 0.05 ). Furthermore, AGTRAP was associated with immune response and may change immune microenvironment in HCC ( P < 0.05 ). Next, gene enrichment analysis suggested that AGTRAP may be involved in the biological process, such as cotranslational protein targeting to the membrane. Finally, we identified the oncogenic effect of AGTRAP by qRT-PCR, colony formation, western blot, and CCK-8 assay ( P < 0.05 ). Conclusion. We provided robust evidences that a stratification system based on AGTRAP can guide survival prediction for HCC patients.

2021 ◽  
Author(s):  
Rui Feng ◽  
Jian Li ◽  
Weiling Xuan ◽  
Hanbo Liu ◽  
Dexin Cheng ◽  
...  

Abstract Background Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer and the main cause of cancer mortality. Its high complexity and dismal prognosis bring dramatic difficulty to treatment. Due to the disclosed dual functions of autophagy in cancer development, understanding autophagy-related genes devotes into seeking novel biomarkers for HCC. Methods Differential expression of genes in normal and tumor groups was analyzed to acquire autophagy-related genes in HCC. GO and KEGG pathway analyses were conducted on these genes. Genes were then screened by univariate regression analysis. The screened genes were subjected to multivariate Cox regression analysis to build a prognostic model. The model was validated by ICGC validation set. Results Altogether, 42 autophagy-related differential genes were screened by differential expression analysis. Enrichment analysis showed that they were mainly enriched in pathways including regulation of autophagy and cell apoptosis. Genes were screened by univariate analysis and multivariate Cox regression analysis to build a prognostic model. The model was constituted by 6 feature genes: EIF2S1, BIRC5, SQSTM1, ATG7, HDAC1, FKBP1A. Validation confirmed the accuracy and independence of this model in predicting HCC patient’s prognosis. Conclusion A total of 6 feature genes were identified to build a prognostic risk model. This model is conducive to investigating interplay between autophagy-related genes and HCC prognosis.


2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Xueliang Zhou ◽  
Mengmeng Dou ◽  
Zaoqu Liu ◽  
Dechao Jiao ◽  
Zhaonan Li ◽  
...  

Background. Hepatocellular carcinoma (HCC) remains an important cause of cancer death. The molecular mechanism of hepatocarcinogenesis and prognostic factors of HCC have not been completely uncovered. Methods. In this study, we screened out differentially expressed lncRNAs (DE lncRNAs), miRNAs (DE miRNAs), and mRNAs (DE mRNAs) by comparing the gene expression of HCC and normal tissue in The Cancer Genome Atlas (TCGA) database. DE mRNAs were used to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the miRNA and lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a competitive endogenous RNA (ceRNA) network by weighted gene coexpression network analysis (WGCNA). Moreover, univariable Cox regression and Kaplan-Meier curve analyses of DE lncRNAs and DE mRNAs were conducted. Finally, the lasso-penalized Cox regression analysis and nomogram model were used to establish a new risk scoring system and predict the prognosis of patients with liver cancer. The expression of survival-related DE lncRNAs was verified by qRT-PCR. Results. A total of 1896 DEmRNAs, 330 DElncRNAs, and 76 DEmiRNAs were identified in HCC and normal tissue samples. Then, the turquoise miRNA and turquoise lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a ceRNA network by WGCNA. In this ceRNA network, there were 566 lncRNA-miRNA-mRNA regulatory pairs, including 30 upregulated lncRNAs, 16 downregulated miRNAs, and 75 upregulated mRNAs. Moreover, we screened out 19 lncRNAs and 14 hub mRNAs related to prognosis from this ceRNA network by univariable Cox regression and Kaplan-Meier curve analyses. Finally, a new risk scoring system was established by selecting the optimal risk lncRNAs from the 19 prognosis-related lncRNAs through lasso-penalized Cox regression analysis. In addition, we established a nomogram model consisting of independent prognostic factors to predict the survival rate of HCC patients. Finally, the correlation between the risk score and immune cell infiltration and gene set enrichment analysis were determined. Conclusions. In conclusion, the results may provide potential biomarkers or therapeutic targets for HCC and the establishment of the new risk scoring system and nomogram model provides the new perspective for predicting the prognosis of HCC.


2020 ◽  
Vol 11 ◽  
Author(s):  
Jian-Rong Sun ◽  
Chen-Fan Kong ◽  
Kun-Min Xiao ◽  
Jia-Lu Yang ◽  
Xiang-Ke Qu ◽  
...  

Hepatocellular carcinoma (HCC) is one of the most common types of malignancy and is associated with high mortality. Prior research suggests that long non-coding RNAs (lncRNAs) play a crucial role in the development of HCC. Therefore, it is necessary to identify lncRNA-associated therapeutic biomarkers to improve the accuracy of HCC prognosis. Transcriptomic data of HCC obtained from The Cancer Genome Atlas (TCGA) database were used in the present study. Differentially expressed RNAs (DERNAs), including 74 lncRNAs, 16 miRNAs, and 35 mRNAs, were identified using bioinformatics analysis. The DERNAs were subsequently used to reconstruct a competing endogenous RNA (ceRNA) network. A lncRNA signature was revealed using Cox regression analysis, including LINC00200, MIR137HG, LINC00462, AP002478.1, and HTR2A-AS1. Kaplan-Meier plot demonstrated that the lncRNA signature is highly accurate in discriminating high- and low-risk patients (P &lt; 0.05). The area under curve (AUC) value exceeded 0.7 in both training and validation cohort, suggesting a high prognostic potential of the signature. Furthermore, multivariate Cox regression analysis indicated that both the TNM stage and the lncRNA signature could serve as independent prognostic factors for HCC (P &lt; 0.05). Then, a nomogram comprising the TNM stage and the lncRNA signature was determined to raise the accuracy in predicting the survival of HCC patients. In the present study, we have introduced a ceRNA network that could contribute to provide a new insight into the identification of potential regulation mechanisms for the development of HCC. The five-lncRNA signature could serve as a reliable biosignature for HCC prognosis, while the nomogram possesses strong potential in clinical applications.


BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Liang Luo ◽  
Haiyi Zhou ◽  
Hao Su

Abstract Background The tumor microenvironment acts a pivotal part in the occurrence and development of tumor. However, there are few studies on the microenvironment of papillary renal cell carcinoma (PRCC). Our study aims to explore prognostic genes related to tumor microenvironment in PRCC. Methods PRCC expression profiles and clinical data were extracted from The Cancer Gene Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Immune/stromal scores were performed utilizing the ESTIMATE algorithm. Three hundred fifty-seven samples were split into two groups on the basis of median immune/stromal score, and comparison of gene expression was conducted. Intersect genes were obtained by Venn diagrams. Hub genes were selected through protein-protein interaction (PPI) network construction, and relevant functional analysis was conducted by DAVID. We used Kaplan–Meier analysis to identify the correlations between genes and overall survival (OS) and progression-free survival (PFS). Univariate and multivariate cox regression analysis were employed to construct survival model. Cibersort was used to predict the immune cell composition of high and low risk group. Combined nomograms were built to predict PRCC prognosis. Immune properties of PRCC were validated by The Cancer Immunome Atlas (TCIA). Results We found immune/stromal score was correlated with T pathological stages and PRCC subtypes. Nine hundred eighty-nine differentially expressed genes (DEGs) and 1169 DEGs were identified respectively on the basis of immune and stromal score. Venn diagrams indicated that 763 co-upregulated genes and 4 co-downregulated genes were identified. Kaplan-Meier analysis revealed that 120 genes were involved in tumor prognosis. Then PPI network analysis identified 22 hub genes, and four of which were significantly related to OS in patients with PRCC confirmed by cox regression analysis. Finally, we constructed a prognostic nomogram which combined with influence factors. Conclusions Four tumor microenvironment-related genes (CD79A, CXCL13, IL6 and CCL19) were identified as biomarkers for PRCC prognosis.


2020 ◽  
Author(s):  
Xueliang Zhou ◽  
Mengmeng Dou ◽  
Zaoqu Liu ◽  
Dechao Jiao ◽  
Zhaonan Li ◽  
...  

Abstract Background: Hepatocellular carcinoma (HCC) remains an important cause of cancer death. The molecular mechanism of hepatocarcinogenesis and prognostic factors of HCC have not been completely uncovered. Methods: In this study, we screened out differentially expressed lncRNAs (DE lncRNAs), miRNAs (DE miRNAs), and mRNAs (DE mRNAs) by comparing the gene expression of HCC and normal tissue in the The Cancer Genome Atlas (TCGA) database. DE mRNAs were used to perform gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the miRNA and lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a competitive endogenous RNA (ceRNA) network by weighted gene coexpression network analysis (WGCNA). Moreover, univariable Cox regression and Kaplan-Meier curve analyses of DE lncRNAs and DE mRNAs were conducted. Finally, the lasso‐penalized Cox regression analysis and nomogram model were used to establish new risk scoring system and predict the prognosis of patients with liver cancer. Results: A total of 1896 DEmRNAs, 330 DElncRNAs, and 76 DEmiRNAs were identified in HCC and normal tissue samples. Then, the turquoise miRNA and turquoise lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a ceRNA network by WGCNA. In this ceRNA network, there were 566 lncRNA-miRNA-mRNA regulatory pairs, including 30 upregulated lncRNAs, 16 downregulated miRNAs and 75 upregulated mRNAs. Moreover, we screened out 19 lncRNAs and 14 hub mRNAs related to prognosis from this ceRNA network by univariable Cox regression and Kaplan-Meier curve analyses. Finally, a new risk scoring system was established by selecting the optimal risk lncRNAs from the 19 prognosis-related lncRNAs through lasso‐penalized Cox regression analysis. In addition, we established a nomogram model consisting of independent prognostic factors to predict the survival rate of HCC patients. Conclusions: In conclusion, the results may provide potential biomarkers or therapeutic targets for HCC, and the establishment of new risk scoring system and nomogram model provide the new perspective for predicting the prognosis of HCC.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rui Feng ◽  
Jian Li ◽  
Weiling Xuan ◽  
Hanbo Liu ◽  
Dexin Cheng ◽  
...  

Background. Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer. Treatment is dramatically difficult due to its high complexity and poor prognosis. Due to the disclosed dual functions of autophagy in cancer development, understanding autophagy-related genes devotes into novel biomarkers for HCC. Methods. Differential expression of genes in normal and tumor groups was analyzed to acquire autophagy-related genes in HCC. These genes were subjected to GO and KEGG pathway analyses. Genes were then screened by univariate regression analysis. The screened genes were subjected to multivariate Cox regression analysis to build a prognostic model. The model was validated by the ICGC validation set. Results. To sum up, 42 differential genes relevant to autophagy were screened by differential expression analysis. Enrichment analysis showed that they were mainly enriched in pathways including regulation of autophagy and cell apoptosis. Genes were screened by univariate analysis and multivariate Cox regression analysis to build a prognostic model. The model constituted 6 feature genes: EIF2S1, BIRC5, SQSTM1, ATG7, HDAC1, and FKBP1A. Validation confirmed the accuracy and independence of this model in predicting the HCC patient’s prognosis. Conclusion. A total of 6 feature genes were identified to build a prognostic risk model. This model is conducive to investigating interplay between autophagy-related genes and HCC prognosis.


2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Cun-qing Kong ◽  
Xing-cai Chen ◽  
Guan-hua Qiu ◽  
Jing-chen Liang ◽  
Duo Wang ◽  
...  

Background. A growing number of studies have suggested that microRNAs exert an essential role in the development and occurrence of multiple tumours and act as crucial regulators in various biological processes. However, the expression and function of miRNA-140 in hepatocellular carcinoma (HCC) cells are not yet adequately identified and manifested. Methods. The expression of miRNA-140 was determined in HCC tissues and adjacent nontumour tissues by quantitative real-time polymerase chain reaction (qRT-PCR). Kaplan–Meier survival analysis and Cox regression analysis were performed to explore the correlation between miRNA-140 expression level and the survival rate of patients with HCC. Additionally, overexpression experiments were conducted to investigate the biological role of miRNA-140 in HCC cells. Bioinformatics was used to predict the related target genes and pathways of miRNA-140. Results. QRT-PCR results signified that the expression level of miRNA-140 in HCC was lower than that of adjacent normal tissues ( P < 0.0001 ). Compared with the control group, the SMMC-7721 HCC cells in the miRNA-140 mimic group had a decrease in proliferation, migration, and invasion ( P < 0.05 ), whereas those in the miRNA-140 inhibitor group had an increase in proliferation, migration, and invasion ( P < 0.05 ). Cell cycle arrest occurred in the G0/1 phase. Prognosis analysis showed that the expression level of miRNA-140 was not related to the prognosis of HCC. Furthermore, the Kaplan–Meier test revealed that patients with lower miRNA-140 expression levels in liver cancer tissue had significantly shorter disease-free survival (DFS, P = 0.004 ) and overall survival (OS) times ( P = 0.010 ) after hepatectomy. Cox regression analysis further indicated that miRNA-140 was an independent risk factor that may affect the DFS ( P = 0.004 ) and OS times ( P = 0.014 ) of patients after hepatectomy. Our results suggested that miRNA-140 might be a crucial regulator involved in the HCC progression and is thus considered a potential prognostic biomarker and therapeutic target for HCC.


2021 ◽  
Author(s):  
hu panyi ◽  
Yongwei Zhang ◽  
yeben qian

Abstract Background: Objective to evaluate the predictive value of preoperative fibrinogen and systemic inflammatory response index (F-SIRI) in the prognosis of patients with hepatocellular carcinoma after radical hepatectomy. Methods: the clinical data of 298 patients with hepatocellular carcinoma who underwent surgery and confirmed by postoperative pathology in our hospital from January 2015 to December 2017 were retrospectively analyzed. The F-SIRI score was calculated according to FIB and SIRI data of peripheral blood. The relationship between F-SIRI score and clinicopathological characteristics was analyzed. The survival analysis was performed by Kaplan Meier method, Cox regression analysis was used to analyze the prognostic factors. Results: preoperative F-SIRI score was correlated with tumor diameter, FIB and SIRI (P<0.05), but not with age, gender, TNM stage and other clinical features (P>0.05). There were significant differences in the 5-year DFS rate and OS rate among patients with different preoperative F-SIRI scores(P<0.05); Cox regression analysis showed that preoperative tumor diameter, alpha fetoprotein level and F-SIRI score were independent predictors of DFS in patients with HCC (P< 0.05), while preoperative tumor diameter, ALB and F-SIRI score as independent predictors of OS (P<0.05). Conclusion: preoperative F-SIRI is an independent prognostic factor in patients with HCC after radical hepatectomy, with poor prognosis in patients with high level of F-SIRI.


2018 ◽  
Vol 55 (4) ◽  
pp. 343-345 ◽  
Author(s):  
Giovanni Faria SILVA ◽  
Vanessa Gutierrez de ANDRADE ◽  
Alecsandro MOREIRA

ABSTRACT BACKGROUND: The infection for the hepatitis C virus (HCV) is a leading cause of liver-related morbidity and mortality through its evolution to liver cirrhosis, end-stage liver complications and hepatocellular carcinoma. Currently, the new drugs for the HCV infection, based on direct antiviral agents, have changed the outcomes in this setting. OBJECTIVE: To assess death incidence, during the wait for the treatment with the new drugs, and to analyze which independent variable (age, sex, ascite, HDA, albumin, α-fetoprotein, platelets and Meld score) had relation with death. METHODS: Prospective study with cirrhotic patients by HCV. Inclusion: cirrhotic patients by hepatic biopsy (METAVIR), clinic or image, detectable RNA (HCV). Exclusion: Other stages of hepatic fibrosis and hepatocellular carcinoma. Descriptive statistic in continue variables. Fisher Exact and Kaplan Meier and Cox Regression Analysis to assess the association of variables studied with death. P<0.05. RESULTS: A total of 129 patients were included. Of this, 73% were men. Mean age was 57.8±12.1, albumin of 3.5±0.6 mg/dL, platelets of 123.4±59.6 and Meld score of 10.59±3.56. The time of observation was 11.2±3.26 months, and the number of death 9/129 (6,9%). The Kaplan-Meier showed association between death with albumin lower than 2.9 (0.0006), MELD score higher than 15 (0.007) and α-fetoprotein higher than 40 ng/mL (<0.0001). Adjusted Cox Regression Analysis showed that α-fetoprotein higher than 40 ng/ml could be considered an independent risk for death. CONCLUSION: We conclude that, patients with advanced cirrhosis should be prioritized for treatment with direct antiviral agents.


2021 ◽  
Author(s):  
Liang Luo ◽  
Haiyi Zhou ◽  
Hao Su

Abstract Background: The tumor microenvironment acts a pivotal part in the occurrence and development of tumor. However, there are few studies on the microenvironment of papillary renal cell carcinoma (PRCC). Our study aims to explore prognostic genes related to tumor microenvironment in PRCC. Methods: PRCC expression profiles and clinical data were extracted from The Cancer Gene Atlas (TCGA) database. Immune/stromal scores were performed utilizing the ESTIMATE algorithm. 323 samples were split into two groups on the basis of median immune/stromal score, and comparison of gene expression were conducted. Cross genes were obtained by Venn diagrams. Hub genes were selected through protein-protein interaction (PPI) network construction, and relevant functional analysis was conducted by DAVID. We used Kaplan–Meier analysis to identify the correlations between genes and overall survival (OS). Finally, univariate and multivariate cox regression analysis were employed to construct survival model and predict prognosis. Results: We found immune/stromal score was correlated with T pathological grade and PRCC subtypes. 989 differentially expressed genes (DEGs) and 1169 DEGs were identified respectively on the basis of immune and stromal score. Venn diagrams indicated that 763 co-upregulated genes and 4 co-downregulated genes were identified. Kaplan-Meier analysis revealed that 120 genes were involved in tumor prognosis. Then PPI network analysis identified 22 hub genes, and four of which were significantly related to OS in patients with PRCC confirmed by cox regression analysis. Conclusions: Four tumor microenvironment-related genes (CD79A, CXCL13, IL6 and CCL19) were identified as biomarkers for PRCC prognosis.


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